On cherche à étudier l’effet de trois facteurs sur le transcriptome des racines d’Arabidopsis thaliana et de la micro Tomate.

CO2

Clustering

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 9.85664883046411e-11"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.98119243022666e-08"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.9811952723976e-08"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.96855898038484e-13"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 8.95155949365289e-09"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -740101.6
*************************************************
Number of clusters = 12
ICL = -740101.6
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
        22         12         13          4          9         12          9 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
         6         15          9          2         18 

Number of observations with MAP > 0.90 (% of total):
131 (100%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 22        12        13        4         9         12        9        
 (100%)    (100%)    (100%)    (100%)    (100%)    (100%)    (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 6         15        9          2          18        
 (100%)    (100%)    (100%)     (100%)     (100%)    

Model-Based Clustering Using MPLN (Parallelized) Description Performs clustering using mixtures of multivariate Poisson-log normal (MPLN) distribution and model selection using AIC, AIC3, BIC and ICL. Since each component/cluster size (G) is independent from another, all Gs in the range to be tested have been parallelized to run on a seperate core using the parallel R package.

Visualisation en ACP

Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
17.2837  4.7082  0.6320  0.5028  0.2617 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 72.015  19.617   2.633   2.095   1.090 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  72.02   91.63   94.27   96.36   97.45 

(Only 5 dimensions (out of 24) are shown)

NULL

Nitrate

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.59017712728382e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0857992577874072"
[1] "Log-like diff: 0.0273270790755795"
[1] "Log-like diff: 0.00026883443707959"
[1] "Log-like diff: 3.19062152343008e-06"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.21945280540103e-07"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.64516266668124e-08"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.55134596677453e-10"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.000462489060510052"
[1] "Log-like diff: 0.000242301219245178"
[1] "Log-like diff: 4.77362100070877e-05"
[1] "Log-like diff: 1.09183634933174e-05"
[1] "Log-like diff: 2.0185365059433e-06"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 6.74642105237781"
[1] "Log-like diff: 0.748917649480589"
[1] "Log-like diff: 0.443303248886963"
[1] "Log-like diff: 0.190917891083934"
[1] "Log-like diff: 0.073022458493746"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.000229290797541637"
[1] "Log-like diff: 2.07037126571663e-05"
[1] "Log-like diff: 2.86282421768647e-07"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.80115819989635e-05"
[1] "Log-like diff: 6.73545858731472e-06"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.83538203863054e-07"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.005250564818855"
[1] "Log-like diff: 0.000985161822388392"
[1] "Log-like diff: 0.000184812593015948"
[1] "Log-like diff: 3.46787723870534e-05"
[1] "Log-like diff: 6.49753503623174e-06"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3013888
*************************************************
Number of clusters = 12
ICL = -3013888
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
        32         76        176         55         99         47          7 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
        23         26         60         64        172 

Number of observations with MAP > 0.90 (% of total):
833 (99.52%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 32        75        175       55        99        46        7        
 (100%)    (98.68%)  (99.43%)  (100%)    (100%)    (97.87%)  (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 23        25        60         64         172       
 (100%)    (96.15%)  (100%)     (100%)     (100%)    
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
19.1693  3.3531  0.5281  0.3930  0.1205 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 79.872  13.971   2.200   1.638   0.502 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  79.87   93.84   96.04   97.68   98.18 

(Only 5 dimensions (out of 24) are shown)

NULL

Iron

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.06819442180495e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0758628365212921"
[1] "Log-like diff: 0.0198623147528725"
[1] "Log-like diff: 0.00525704057190701"
[1] "Log-like diff: 0.00139919104513453"
[1] "Log-like diff: 0.000341532015648127"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0111170800072813"
[1] "Log-like diff: 0.00141139826298442"
[1] "Log-like diff: 0.000161397428641408"
[1] "Log-like diff: 2.2397745002678e-05"
[1] "Log-like diff: 2.78974421874523e-06"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 986.277045880157"
[1] "Log-like diff: 1238.12354241205"
[1] "Log-like diff: 834.458311448919"
[1] "Log-like diff: 535.055524516963"
[1] "Log-like diff: 353.900755196122"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 61.6607030025399"
[1] "Log-like diff: 220.279999620298"
[1] "Log-like diff: 343.597475596329"
[1] "Log-like diff: 118.657798253033"
[1] "Log-like diff: 89.3377189676476"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1071.58883560517"
[1] "Log-like diff: 800.521834137188"
[1] "Log-like diff: 366.481058739596"
[1] "Log-like diff: 474.616622316664"
[1] "Log-like diff: 2708.0722365157"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 880.312874833506"
[1] "Log-like diff: 275.320585913289"
[1] "Log-like diff: 326.646768080891"
[1] "Log-like diff: 72.2925259231472"
[1] "Log-like diff: 302.279208953793"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 47.7096958256379"
[1] "Log-like diff: 66.4585845237957"
[1] "Log-like diff: 22.8816925488037"
[1] "Log-like diff: 314.666696217909"
[1] "Log-like diff: 181.581898960294"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 18.7472611407226"
[1] "Log-like diff: 4.96990848783927"
[1] "Log-like diff: 0.763933732350862"
[1] "Log-like diff: 0.120476697969336"
[1] "Log-like diff: 0.0199799576709712"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 479.657137925635"
[1] "Log-like diff: 520.602794556823"
[1] "Log-like diff: 257.720945661473"
[1] "Log-like diff: 160.971666718626"
[1] "Log-like diff: 12.0087493319837"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 621.010440786969"
[1] "Log-like diff: 740.364460866503"
[1] "Log-like diff: 532.464606735472"
[1] "Log-like diff: 50.7983333830509"
[1] "Log-like diff: 88.5679701787833"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3391000
*************************************************
Number of clusters = 12
ICL = -3391000
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
       301         71        121        712        122         24        172 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
       256        104         42        307        609 

Number of observations with MAP > 0.90 (% of total):
2759 (97.11%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 288       70        116       705       116       22        167      
 (95.68%)  (98.59%)  (95.87%)  (99.02%)  (95.08%)  (91.67%)  (97.09%) 
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 247       98        40         291        599       
 (96.48%)  (94.23%)  (95.24%)   (94.79%)   (98.36%)  
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
     Ax1      Ax2      Ax3      Ax4      Ax5 
22.03089  1.11520  0.27107  0.11321  0.06945 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
91.7954  4.6467  1.1295  0.4717  0.2894 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  91.80   96.44   97.57   98.04   98.33 

(Only 5 dimensions (out of 24) are shown)

NULL

 

A work by Océane Cassan

oceane.cassan@supagro.fr